Marketing Mix Modeling
Marketing Mix Modeling (MMM) is an econometric methodology that measures the incremental sales impact of each marketing activity. Thru statistical models, marginal ROI is estimated and the budget is optimized across online and offline channels, as well as external factors. It is key to making data-driven decisions without relying on cookies or digital attribution models.
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Budget Optimization with Advanced Econometric Models
With marketing mix modeling (MMM), you’ll identify how much of your sales comes from media and how much from factors like pricing or seasonality. Assign real credit, optimize investment, and guide evidence-based decisions.
Understand the real contribution of each business variable.
MMM analyzes the impact of all variables on your business: from online and offline media to non-advertising factors such as seasonality, weather, or macroeconomic indicators. This comprehensive statistical analysis enables understanding of each variable’s specific contribution, giving marketing teams a clear view of actual performance.
Current Challenges in Marketing Attribution
Reduced Effectiveness of Individual Data in a Cookie-Free Environment
Increasing privacy and the reduced availability of cookies limit the accuracy of models based on individual data. MMM operates on anonymized, aggregated data, offering a robust and statistically reliable alternative.
The need for incremental and full-funnel measurement.
The complexity of the media ecosystem demands models that integrate exogenous variables such as the economy, seasonality, and promotions. MMM quantifies how each channel contributes to the final outcome thru incremental and full-funnel analysis.
Need for an incremental, full-funnel approach
The multiplicity of media requires quantifying in greater detail how each channel contributes to the final outcome, incorporating exogenous variables (seasonality, the economy, promotions).
Collection and Normalization of Internal and External Data
We centralize, cleanse, and normalize your internal and external data (such as weather, economic indicators, or seasonality) to ensure models with high statistical reliability. This solid foundation enables precise estimates in the modeling process.
Estimation of the Incremental Impact on Sales and ROI
We quantify the incremental contribution of each independent variable to your business’s sales. This estimate allows you to understand the marginal impact and discover where an additional investment generates the greatest return.
Convergence of ROAS with Statistical Iterations
We applied multiple iterations and models until we obtained those with the best fit and statistical significance. This process ensures robust ROAS estimates, which are essential for reliable investment decisions.
Saturation Curves to Identify Optimal Investment Levels
We determine channel-specific media saturation curves to identify points of diminishing returns. This information is key to adjusting investment and avoiding overexposing channels with low marginal returns.
Budget Allocation and ROAS Optimization
We calculate comparative scenarios between your current budget and the model’s suggested optimized allocation. This reveals redistribution opportunities that maximize ROI, ROAS, and overall profitability of the media mix.
Continuous Model Calibration and Results Validation
We integrate incrementality studies, periodic optimizations, and statistical controls to calibrate the MMM and ensure that the results remain consistent over time. This ensures accuracy and stability in strategic recommendations.
Our differentiating value
Bunker DB’s Marketing Mix Modeling module provides an objective and holistic view of the actual impact of each channel—digital and offline—on business outcomes. Based on aggregated data and advanced statistical models, it overcomes the limitations of traditional attribution and enables you to optimize investment, project future scenarios, and make decisions with complete confidence.
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